Segment Anything Model for Medical Image Analysis: an Experimental Study (2023-04-20T00:00:00.000000Z)

TL;DR

An extensive evaluation of Segment Anything Model's ability to segment medical images on a collection of 19 medical imaging datasets from various modalities and anatomies concludes that SAM shows impressive zero-shot segmentation performance for certain medical Imaging datasets, but moderate to poor performance for others.

Authors

N. Konz

2 papers

M. Mazurowski

1 papers

Haoyu Dong

1 papers

Han Gu

1 papers

Jichen Yang

1 papers

TL;DR

Authors

References43 items

SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM

Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Segment Anything

GPT-4 Technical Report

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

SimpleClick: Interactive Image Segmentation with Simple Vision Transformers

A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions

FocalClick: Towards Practical Interactive Image Segmentation

Exploring Plain Vision Transformer Backbones for Object Detection

CT2US: Cross-modal transfer learning for kidney segmentation in ultrasound images with synthesized data.

Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Knowledge-aware Zero-Shot Learning: Survey and Perspective

Reviving Iterative Training with Mask Guidance for Interactive Segmentation

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Dataset of breast ultrasound images

Deep High-Resolution Representation Learning for Visual Recognition

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

The Liver Tumor Segmentation Benchmark (LiTS)

A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features

Iteratively Trained Interactive Segmentation

Medical Image Analysis using Convolutional Neural Networks: A Review

Spinal cord grey matter segmentation challenge

Deep Learning for Medical Image Processing: Overview, Challenges and Future

CNN-based Segmentation of Medical Imaging Data

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

Two public chest X-ray datasets for computer-aided screening of pulmonary diseases.

Medical Image Analysis

Accuracy of Segment-Anything Model (SAM) in medical image segmentation tasks

Segment Anything in Medical Images

A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation

Fully automated deep learning method for fibroglandular tissue segmentation in breast

X-ray images of the hip joints

Y-90 pet/ct & spect/ct and corresponding contours dataset 31july2020

CT-ORG: A Dataset of CT Volumes

Hasnin, kaggle446, shirzad, Cukierski Will, and yffud

Current methods in medical image segmentation.

The OpenCV Library. Dr. Dobb's Journal of Software Tools

Publisher: Mendeley Data

OpenCV: Miscellaneous Image Transformations

Field of Study

Journal Information

Name

Page

Volume